

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 	a (     A__      :  Union[str, Any] ,  A__      :  List[Any] ,  A__      :  Dict	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=MobileBertConfig.from_json_file(_lowerCamelCase	)
      print(F'''Building PyTorch model from configuration: {config}'''	)
      _lowercase      			=MobileBertForPreTraining(_lowerCamelCase	)
      # Load weights from tf checkpoint
      _lowercase      			=load_tf_weights_in_mobilebert(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase	)
      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
      torch.save(model.state_dict() ,  _lowerCamelCase	)


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--mobilebert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained MobileBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase_   							=  parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")